Drive, Drift, or be Data-Driven

We are living in a data-driven world where businesses operate under strict laws and regulations related to data. Companies across all industries including financial services, healthcare, insurance, telecommunication, and many others, are treating data as an enterprise asset in everyday practice. Many companies go through regular federal audits concerning business model, risk assessment, and reporting. This helps to ensure that a given business complies with standards defined by the federal authorities. Failing to meet the requirements leads to fines in terms of monetary value and potential lawsuits, not to mention reputational damage. We saw this a lot during the 2008 financial crisis, when many large financial institutions faced the threat of collapse.

For sure, data is in the spotlight. But the attention comes through a whip of strict policies and procedures. Businesses are shifting gears, moving faster, beating competition, and adopting to the culture of being data-driven. Some even say they are data-driven, but in reality, very few have reached that pinnacle.

For instance, look at Amazon. They brought a revolution to the e-commerce business and now hold the title of #1 retailer in the world. How’s that even possible? The answer lies in its sustainable business model built around customer needs and purely driven by data. Recently, a friend asked if I can suggest an English grammar book. So I did a quick search on Amazon. Within few minutes, I was able to recommend a book (see it for yourself: The Elements of Style). The results were so descriptive in terms of popularity, reviews, and rating. And as a result, it helped me find the right book for my friend.

Another example is Airbnb. It’s a story about three roommates who expanded their business from renting air mattresses in an apartment to a $10 billion dollar company. What’s their secret sauce? Airbnb used data to personalize traveling experience.

Let’s pause here for a second. Do we know anything about our data and the insight we need for being data-driven?

As we accelerate into the future, without any choice, we must be more precise in terms of collecting and using information. But how do we get there? Of course, not by mistrust in data, dealing with human errors, and inaccuracy of data. We will march and roll in with the flag of data governance. This translates into building acceptable data standards and procedures, controlling variation, and building a mechanism where people can easily find, understand, and use information they need. Hence, it means facilitating a good grip around data and helping understand its dynamics, including:

Where it resides

How it moves

Who uses it

For what purposes

A Simple Example

Let me give you a small picture of what that looks like mainly in relationship-focused businesses, especially those with customer contracts. Examples include traveling, banking and insurance services, telecommunications, and most of the business-to-business sector. In the sample diagram below, I’m showing a Customer Lifetime Value report and its traceability all the way to its source system(s): Here I can find a report, understand how it is built using attributes, corresponding data quality score and its traceability to source systems and importantly, use it with full trust.

To give you more context, “Customer Lifetime Value (CLTV) is a report that represents the total net profit a company makes from any given customer (source – TechTarget)”. We calculate it using the formula:

The formula includes attributes (discount rate, profit contributions, retention rate, etc) that make up CLTV report (also shown in the diagram along with the data quality score). Businesses use CLTV formula to determine how much each customer is worth in dollar value. And therefore, they determine exactly how much a marketing department should be willing to spend to acquire each customer. For example, if a new customer costs $10 to acquire, and their lifetime value is $20, then the company will determine the customer is profitable.

Focusing on better governance around data helps establish ownership, clear understanding of data, and most important, a control point to understand and manage the dynamics of data. Hence, as an organization increases its control around data, its key asset, it increases the ability to make the right decisions for the business, customers, and data citizens.

Kash is a Customer Advisory Manager at Collibra. He is involved in research, development, and delivery of enterprise data governance solutions. He is also an instructor for Collibra University, a worldwide community of 1000+ users. Prior to joining Collibra, he was a researcher at University of Arkansas for Medical Sciences, a leading cancer research institute. Kash also has a masters degree in Information Quality from University of Arkansas for Little Rock in collaboration with MIT. He has published his research findings at ICIQ, SE Regional IDeA and ITNG conferences.

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Collibra makes it easy for data citizens to find, understand and trust the organizational data they need to make business decisions every day. Unlike traditional data governance solutions, Collibra is a cross-organizational platform that breaks down the traditional data silos, freeing the data so all users have access.

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